Engineering Manager, ML Platform

About the Role & Team

The Machine Learning Platform team at Penn Entertainment is dedicated to building and maintaining the infrastructure, tools, and frameworks that support our data science and machine learning initiatives. As the Engineering Manager of the ML Platform, you will lead a team of talented engineers, fostering a culture of collaboration, innovation, and technical excellence. You will be responsible for guiding the team in developing scalable machine learning platforms, optimizing model deployment processes, and implementing best practices in machine learning operations (MLOps).

About the Work

• Drive the vision, strategy, and execution of the machine learning platform, ensuring alignment with data science and machine learning deliverables at the company.
• Hire, mentor, and lead a team of ML platform engineers, fostering professional growth and a culture of innovation.
• Oversee the development of scalable ML infrastructure, model serving, feature stores, and monitoring systems.
• Partner with stakeholders across data engineering, ML engineering, and data product teams to enable the development of robust ML pipelines and integrations
• Establish and improve MLOps best practices, ensuring robust CI/CD pipelines, model monitoring, and efficient resource utilization.
• Assess and recommend new tools, frameworks, and technologies to improve the efficiency and effectiveness of machine learning solutions.
• Maintain technical design documents, Git/Confluence documentation, and ensure knowledge sharing across the team.

About You

• Experience: 3+ years of professional experience in machine learning, data engineering, or software engineering, with at least 2 years in a leadership or managerial role.
• Strong background in machine learning infrastructure, data engineering, and software development, with proficiency in Python, SQL, and cloud platforms (GCP, AWS, Azure).
• Experience with machine learning packaging and serving patterns and related tooling (MLflow, Seldon, Kserve, Vertex, AWS SageMaker)
• Hands-on experience with CI/CD pipelines, containerization (Docker, Kubernetes), and orchestration tools (Airflow, Kubeflow, or Dagster).
• Ability to communicate complex technical concepts to non-technical stakeholders and lead a high-performing team.
• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.

Nice to Have

• Experience with real-time stream processing and virtual feature stores (Featureform, Feast).
• Background in deploying and monitoring large language models (LLMs).
• Familiarity with BI tools such as Tableau, Looker, or Mode.

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